Prediction of M2 with early-stage hepatocellular carcinoma based on Nomogram

doi:10.21203/rs.3.rs-5242545/v1

Background

Microvascular invasion (MVI) is a crucial factor for early recurrence and poor outcomes in hepatocellular carcinoma (HCC). However, there are few studies on M2 classification. We aimed to build a predictive model for M2 in early-stage HCC, assisting clinical decision-making.

Methods

We retrospectively enrolled 451 patients with early-stage HCC and employed multiple machine learning algorithms to identify the risk factors influencing the robustness of M2. Model performance was evaluated using receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).

Results

There were 363 M0-1 and 88 M2 cases. Differences in recurrence-free survival(RFS) and overall survival(OS) between the M0-1 and M2 groups were statistically significant (P < 0.0001). Complement C3, tumor size > 5cm, incomplete tumor capsule, and Edmondson-Steiner stage III-IV were independent risk factors for M2.The prediction model showed an area under the receiver operating characteristic curve(AUROC) of 0.765 and 0.807 in the training and validation groups, respectively. Calibration curves showed good agreement between actual and predicted M2 risks, and the DCA and CIC showed a significant clinical efficacy.

Conclusion

The nomogram-based model had a good predictive effect for M2 in patients with early-stage HCC ,providing guidance for treatment decisions.

Hepatocellular carcinoma (HCC)

Microvascular invasion

early-stage

machine learning

nomogram

Hepatocellular carcinoma (HCC) is the most common liver malignancy, ranking sixth and third in terms of global morbidity and mortality, respectively. [1, 2] It is well known that HCC in stage Barcelona Clinic Liver Cancer-A(BCLC-A) is defined as early-stage.^[3^] In the 2022 update of the BCLC prognosis prediction and treatment strategy, stage BCLC 0 (normal liver function, single tumor, 2 cm diameter, no vascular invasion or extrahepatic metastasis) was defined as very early-stage. The first-line treatment strategy for HCC in stage BCLC 0/A is surgical resection, ablation and liver transplantation.^[4^] Despite some improvements in the diagnosis and treatment of HCC, the proportion of patients with early-stage HCC is < 30%;together with the high invasion and heterogeneity of HCC, the overall recurrence rate at 3 years remains as high as 30–50%, leading to a poor prognosis.^[5^, 6^] Microvascular invasion (MVI) mainly refers to the cancer cell nests that can only be observed under a microscope in the lumen of the tiny vessels lined with endothelial cells, mostly in the tumor envelope and adjacent liver tissue. [7]According to the quantity and distribution of cancer cell nests, MVI can be categorized into three levels: M0 level, where no MVI is detected; M1 level, characterized by ≤ 5 MVIs occurring in the peri-cancerous liver tissue region (≤ 1cm); and M2 level, defined by > 5 MVIs or MVIs occurring in the distant peri-cancerous liver tissue region (> 1cm). ^[8^, 9^] MVI is associated with various factors, including the invasive ability of tumor cells, the secretion of angiogenic factors, and immune escape. Tumor cells degrade the vascular basement membrane via secreted proteases, which then invade the vascular lumen to form cancer cell nests. Simultaneously, tumor cells secrete angiogenic factors, promote the formation of neovascularization, and provide conditions for tumor growth and metastasis. Numerous studies have confirmed that MVI is an important factor affecting the recurrence and survival of patients with liver cancer, and personalized treatment based on MVI is of great significance for improve the surgical outcomes of patients with HCC.[10, 11] Studies have shown that patients with M2 have a poorer prognosis after radical resection than those with M0 and M1 ^[8^, 12^], and M2 is a high-risk factor for postoperative residual cancer recurrence and intrahepatic metastasis ^[13^, 14^].Therefore, we should focus not only on the presence or absence of MVI, but also on the M2. If preoperative patients requiring liver resection are judged to have a higher risk of M2, it is recommended to expand the margin or advance the intervention for transformation treatment to prevent early postoperative recurrence and metastasis thus improving patient prognosis.[15, 16]

Machine learning has been widely used in research on HCC. Machine learning is more accurate, more stable, and more fully high-throughput and multi-dimensional clinical data mining, so it can improve the performance of prediction models.^[17^] In recent years, with the development of radiomics, an increasing number of radiomic features have been considered to predict MVI and judge the prognosis of HCC.^[18^, 19^]Although many prediction models have been used to predict MVI, there is a large heterogeneity in the criteria for HCC inclusion, and the risk prediction studies on M2 remain scarce.^[20–22^] In this study, we screened clinical indicators of HCC through machine learning, established a prediction model for early-stage HCC patients with M2, and drew a nomogram for stratified intervention to reduce postoperative recurrence, which could be beneficial for clinicians.

Methods

The clinical data of 888 patients diagnosed with HCC from December 2012 to December 2018 at the Affiliated Cancer Hospital of Guangxi Medical University were collected. The inclusion criteria were as follows: 1) BCLC 0/A stage, 2) hepatitis B surface antigen (HBsAg) (+), and 3) MVI grade M0,M1 and M2.The exclusion criteria were as follows: 1) imaging and blood index examination 1 week before surgery, 2) non-R0 resection, 3) preoperative surgery, ablation, radiofrequency, or neoadjuvant therapy, 4) MVI status not assessed, and 5) incomplete clinical data. Finally, the clinical data of 451 patients were included, and the number of seeds was set using R software. The patients were divided into training and validation groups at a 7:3 ratio. The training group was used to build the prediction model, and the validation group was used for internal validation (Fig. 1). This was a retrospective and controlled study. This study adhered to the Declaration of Helsinki and was approved by the Ethics Committee of Guangxi Medical University Cancer Hospital(Batch No:KY2024454).

Data collection and processing

Basic patient information was collected, including data on gender, age, C-reactive protein, D-dimer, complement C3, T helper cells, inhibitory T cells, natural killer cells, B-lymphocytes, total-bilirubin, total-protein, glutamic-pyruvic transaminase, glutamic-oxalacetic transaminase, alpha fetoprotein (AFP), platelet count, absolute neutrophil values, absolute monocyte values, absolute lymphocyte values, prothrombin time, cirrhosis, ascites, tumor size, number of tumors, tumor capsule, BCLC-stages, Edmondson-Steiner classification, recurrence, recurrence-free survival(RFS), survival and overall survival(OS). Postoperative tissue specimens were further examined pathologically to confirm the presence of MVI and subsequently graded. Liver cirrhosis was defined as B-ultrasound liver transient elastography with an elasticity value (or hardness value) > 7.1 kPa. RFS as defined as the time interval from the start of surgical treatment to the first recurrence of the tumor. OS was defined as the time from the diagnosis of HCC to death from any cause.

After tumor resection, all patients were followed-up regularly according to clinical guidelines. Follow-ups included laboratory tests (serum AFP level and liver function tests) and imaging studies (ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI)) once every 3 months after surgery. The diagnosis of HCC recurrence was based on two or more examination methods, including ultrasonography, CT, MRI, and hepatic arteriography, with elevated serum AFP levels. The patients with confirmed recurrent HCC underwent further evaluation by a multidisciplinary team. The last follow-up was conducted on 31 December 2020.

Screening for indicators related to M2

The Least Absolute Shrinkage and Selection Operator (LASSO) is a regularization algorithm used for linear regression and related problems. It achieves a sparseness of the model coefficients by adding L1 regular terms to the loss function, thus prompting the model to select relatively few features. [23]The Boruta algorithm is a machine learning algorithm used for feature selection, which is especially suitable for the random forest model. The algorithm determines the threshold by ranking the feature importance. This threshold will be used to distinguish which features are "important" and which are "not". [24]XGBoost (eXtreme Gradient Boosting) is a machine learning algorithm for gradient-boosting trees that has achieved significant improvements in prediction performance and computing efficiency. This provides a means for assessing the importance of these features. The relative importance of each feature can be obtained by measuring the number of splittings constructing the tree or the contribution of the feature to the target variable during splitting.[25] The Best subset algorithm is a feature-selection method designed to select the best subset from a given set of features to build a linear regression model. The algorithm evaluates the performance of each subset by exhaustively imposing all possible feature combinations and selecting the subset with the most influence on the target. [26]

SHAP (SHapley Additive exPlanations) is a model used to interpret machine learning model prediction results. The SHAP values are based on the Shapley-value concept in game theory, which provides a fair, consistent, and efficient way to assign contributing values to each feature, thereby explaining how the model's output for each sample is formed. For each feature, the SHAP value considers all possible subset combinations that the feature may have formed with other features and calculates the average contribution of the feature to the output in these combinations. [27]

The clinical characteristic factors derived from the four machine algorithms were intersected, and a Venn diagram was created. Ultimately, the analysis identified the definitive predictive factors associated with M2. These factors were used to construct the nomogram. The nomogram measures each regression coefficient in the logistic regression on a scale of 0–100 points. The points for each independent variable were summed, and the predicted probabilities were obtained from the total points. The predictive performance and accuracy of the nomograms were evaluated using the area under the receiver operating characteristic curve(AUROC) and calibration curves, respectively. Decision curve analysis (DCA) and clinical impact curve (CIC) were used to assess the clinical utility of the nomogram by calculating the net benefit of different threshold probability points.

Statistical analysis

Normally distributed continuous variable data were presented as the mean ± standard deviation (SD), and comparisons between the two groups were performed using the Student’s t-test. Counts were described as numbers and percentages (%), and comparisons between the two groups were performed using the χ2 test. Logistic regression was used to conduct the multivariate analyses. RFS and OS were calculated using Kaplan–Meier curves, which were compared using the log-rank test. All statistical tests were two-tailed, and P < 0.05 was considered statistically significant. The data were processed using version SPSS 26.0 (IBM, New York, USA) and version R software (version 4.3.2, R Project for Statistical Computing) with R software packages“Boruta”,“glmnet”,“leaps”,“xgboost”and“shapviz”.

Baseline characteristics

In total, 451 patients with HCC were retrospectively included in this study. There were 363 (80.49%) M0-1 patients with a mean age of 51.1 years, and 88 (19.51%) M2 patients with a mean age of 52.0 years. Detailed clinical case characteristics are shown in (Table 1). A baseline comparison of the clinical case characteristics between the training group (n = 317) and validation group (n = 134) showed that the differences in all metrics were not statistically significant (P > 0.05) (Table 2).

Table 1

Comparison of baseline data among M0-1 VS M2 stage HCC patients
Variables	Levels	M0-1 (N = 363)	M2 (N = 88)	p-value
Age(year)	Mean ± SD	51.1 ± 11.6	52.0 ± 10.4	0.447
CRP(mg/L)	Mean ± SD	7.2 ± 14.2	8.8 ± 12.7	0.305
D.dimer(µg/mL)	Mean ± SD	1.1 ± 1.7	1.1 ± 1.3	0.987
C3(g/L)	Mean ± SD	1.0 ± 0.3	0.9 ± 0.2	0.001
Th Cells(%)	Mean ± SD	38.9 ± 8.0	38.2 ± 7.9	0.482
Treg Cells(%)	Mean ± SD	21.0 ± 6.9	20.4 ± 5.6	0.320
NK Cells(%)	Mean ± SD	15.1 ± 7.9	14.5 ± 8.6	0.516
B.Lymphocytes(%)	Mean ± SD	12.8 ± 5.4	12.6 ± 5.1	0.699
TBIL(µmol/L)	Mean ± SD	14.8 ± 8.3	15.4 ± 9.8	0.605
ALB(g/L)	Mean ± SD	38.7 ± 4.1	37.7 ± 3.8	0.052
ALT(U/L)	Mean ± SD	46.0 ± 50.6	37.3 ± 19.5	0.081
AST(U/L)	Mean ± SD	46.5 ± 38.0	45.8 ± 22.6	0.805
Platelet(*10⁹/L)	Mean ± SD	196.8 ± 75.4	203.0 ± 70.8	0.462
Neutrophil(*10⁹/L)	Mean ± SD	3.6 ± 1.5	3.8 ± 1.5	0.260
Monocyte(*10⁹/L)	Mean ± SD	0.5 ± 0.4	0.5 ± 0.2	0.457
Lymphocytes (*10⁹/L)	Mean ± SD	1.8 ± 0.6	1.7 ± 0.6	0.028
AFP(ng/ml)	≤ 400	264 (72.7%)	49 (55.7%)	0.003
	>400	99 (27.3%)	39 (44.3%)
HBV-DNA(IU/ml)	≤ 10³	179 (49.3%)	43 (48.9%)	1.000
	>10³	184 (50.7%)	45 (51.1%)
Gender	Female	61 (16.8%)	7 (8%)	0.055
	Male	302 (83.2%)	81 (92%)
PT(s)	≤ 13	230 (63.4%)	62 (70.5%)	0.260
	>13	133 (36.6%)	26 (29.5%)
Ascites(ml)	≤ 20	322 (88.7%)	70 (79.5%)	0.035
	>20	41 (11.3%)	18 (20.5%)
Tumor-Size(cm)	≤ 5	220 (60.6%)	30 (34.1%)	< 0.001
	>5	143 (39.4%)	58 (65.9%)
Cirrhosis	No	86 (23.7%)	11 (12.5%)	0.032
	Yes	277 (76.3%)	77 (87.5%)
Tumor-Number	Single	340 (93.7%)	87 (98.9%)	0.061
	Multiple	23 (6.3%)	1 (1.1%)
Tumor-Capsule	Complete	326 (89.8%)	65 (73.9%)	< 0.001
	Incomplete	37 (10.2%)	23 (26.1%)
BCLC-stage	0-Stage	29 (8%)	1 (1.1%)	0.038
	1-Stage	334 (92%)	87 (98.9%)
Edmondson-stage	I-II	204 (56.2%)	23 (26.1%)	< 0.001
	III-IV	159 (43.8%)	65 (73.9%)
Recurrence	No	227 (62.5%)	40 (45.5%)	0.005
	Yes	136 (37.5%)	48 (54.5%)
RFS(mon)	Mean ± SD	16.0 ± 14.2	10.4 ± 11.2	< 0.001
Survival	Live	304 (83.7%)	50 (56.8%)	< 0.001
	Dead	59 (16.3%)	38 (43.2%)
OS(mon)	Mean ± SD	38.4 ± 15.7	25.8 ± 12.7	< 0.001
Abbreviations: CRP, C-reactive protein;C3, Complement C3; Th Cells, CD3 + CD4 + T Cells; Treg Cells, CD3 + CD8 + T Cells; NK Cells, Natural Killer Cells; AFP, alpha fetoprotein; Edmondson-stage, Edmondson-Steiner stage;RFS, recurrence-free survival;OS, overall survival.

Table 2

Comparison of baseline data between training group and validation group for HCC patients
Variables	Number(%)/Mean(SD)		p-value
	Training-Group (N = 317)	Validation-Group (N = 134)
Age(year)	51.2 (11.4)	51.4 (11.4)	0.842
CRP(mg/L)	7.38 (13.9)	7.80 (14.3)	0.779
D.dimer(µg/mL)	1.13 (1.83)	0.89 (1.06)	0.081
C3(g/L)	0.94 (0.25)	0.95 (0.27)	0.821
Th Cells(%)	38.9 (7.93)	38.5 (8.09)	0.642
Treg Cells(%)	21.2 (6.79)	20.3 (6.31)	0.224
NK Cells(%)	14.9 (7.68)	15.4 (8.87)	0.576
B.Lymphocytes(%)	12.9 (5.50)	12.6 (5.08)	0.640
TBIL(µmol/L)	14.6 (7.88)	15.9 (10.0)	0.170
ALB(g/L)	38.5 (4.00)	38.4 (4.26)	0.732
ALT(U/L)	45.1 (52.2)	42.3 (28.1)	0.457
AST(U/L)	47.7 (40.2)	43.4 (20.5)	0.137
Platelet(*10⁹/ml)	201 (78.7)	191 (63.2)	0.169
Neutrophil(*10⁹/ml)	3.68 (1.51)	3.49 (1.37)	0.176
Monocyte(*10⁹/ml)	0.47 (0.17)	0.51 (0.57)	0.385
Lymphocytes(*10⁹/ml)	1.82 (0.64)	1.79 (0.62)	0.562
AFP(ng/ml)			0.738
≤ 400	222 (70.0%)	91 (67.9%)
>400	95 (30.0%)	43 (32.1%)
HBV-DNA(IU/ml)			0.612
≤ 10³	159 (50.2%)	63 (47.0%)
>10³	158 (49.8%)	71 (53.0%)
Gender			1.000
Female	48 (15.1%)	20 (14.9%)
Male	269 (84.9%)	114 (85.1%)
PT(s)			0.786
≤ 13	207 (65.3%)	85 (63.4%)
>13	110 (34.7%)	49 (36.6%)
Ascites(ml)			0.535
≤ 20	273 (86.1%)	119 (88.8%)
>20	44 (13.9%)	15 (11.2%)
Tumor-Size(cm)			0.382
≤ 5	171 (53.9%)	79 (59.0%)
>5	146 (46.1%)	55 (41.0%)
Cirrhosis			0.502
No	65 (20.5%)	32 (23.9%)
Yes	252 (79.5%)	102 (76.1%)
Tumor-Number			1.000
Single	300 (94.6%)	127 (94.8%)
Multiple	17 (5.36%)	7 (5.22%)
Tumor-Capsule			0.838
Complete	276 (87.1%)	115 (85.8%)
Incomplete	41 (12.9%)	19 (14.2%)
BCLC-stage			0.138
0	17 (5.36%)	13 (9.70%)
1	300 (94.6%)	121 (90.3%)
Edmondson-stage			0.416
I-II	164 (51.7%)	63 (47.0%)
III-IV	153 (48.3%)	71 (53.0%)

Comparison of RFS and OS M0-1 VS M2 stage HCC patients.

The median follow-up time was 42.0 months (range: 2–63 months). The median RFS in the M0-1 group was 30.8 months, and the median OS was not achieved. The median RFS in the M2 group was 10.4 months and the median OS was 43.1 months. The hazard ratio (HR) of M0-1 RFS compared to M2 was 2.149 (95% confidence interval (CI): 1.542–2.996, P < 0.0001), and the HR of M0-1 OS compared to M2 was 4.348 (95% CI: 2.852–6.63, P < 0.0001) (Fig. 2 and Table S1).

Screening feature variables based on machine learning

The LASSO algorithm screening variables (Fig. 3A, 3B), The coefficients of selected parameters associated with M2 are shown in (Table S2), Boruta algorithm screening variables (Figure S1A), XGboost algorithm screening variables (Figure S1B), and Best_subset algorithm screening variables (Figure S1C) were used to finally determine the C3 level, Tumor-size, Tumor-capsule, and Edmondson–stage classification as the four key variables (Figure S1D).

Construction and validation of model

The four key variables screened using the training set data were included in the multivariate logistic regression analysis and prediction model based on the odds ratio (OR) value and 95% CI (Fig. 4A and Table S3), and a prediction model based on the multivariate logistic regression analysis (Fig. 4B). The AUC in the training set was 0.765 (95% CI: 0.696–0.843) (Fig. 5A) and that in the validation set was 0.807 (95% CI: 0.712–0.903) (Fig. 5B). The prediction results of the calibration curve were close to the actual results in both the training and validation sets, with good consistency (R² = 0.220 and 0.292, respectively; P = 0.864 and 0.590, respectively) (Fig. 5C, 5D). The best cut-off probability obtained using the ROC curve was 0.185, with MVI corresponding to the optimal critical probability value and a total score of 120 points.

Evaluation and application of the model

Finally, the clinical utility of the nomogram model was assessed using DCA and CIC. The results showed that use of a nomogram prediction model within the 0.10–0.60 probability threshold in the training set was beneficial for patients. Furthermore, use of the nomogram prediction model within the 0.05–0.85 probability threshold in the validation set was beneficial for patients (Fig. 6A, 6B). The CIC found that the predictive power of the nomogram model was significant (Fig. 6C, 6D).

Our results indicated that M2 stage in HCC may be associated with lower complement C3 levels, tumor size > 5 cm, incomplete tumor capsules, and late Edmondson–Steiner grade. Using these influencing factors, we developed a nomogram model that could effectively distinguish the presence of M2 in patients with HCC.

The long-term prognosis of patients with HCC remains poor in the early to middle stages after treatment, which is mainly due to the high recurrence rate after primary resection caused by de novo tumors resulting from intrahepatic metastasis or underlying liver pathologies.^[6^, 28^] MVI, considered an important marker of aggressive behavior in HCC, can significantly affect the intrahepatic metastasis of tumor cells through the portal circulation, leading to tumor recurrence after curative surgery. [29]For MVI to occur, the cancer cells must acquire the ability to disrupt ligation/adhesion, decompose the extracellular matrix, and migrate to and utilize alternative energy sources. These changes are commonly observed during epithelial-to-mesenchymal transition (EMT), in which malignant cells de-differentiate from a polar, adherent phenotype to a mobile mesenchymal state with a more aggressive and resilient biology.[30] Increasing evidence suggests that the presence of MVI is associated with detection of the EMT phenotype in primary tumors. In MVI-positive cases, M2 grade is an obvious indicator of poor prognosis in HCC.[31] Myeloid cells and macrophages in the tumor microenvironment of HCC promote MVI and tumor progression through midkine proteins, providing favorable conditions for rapid tumor growth and invasion. [32]Unfortunately, M2 classification can only be diagnosed by histopathological examination after surgical resection; therefore, we need to look for important risk factors for MVI and its M2 grade and develop predictive models to make optimal clinical decisions, which may have important implications for postoperative recurrence detection and anti-recurrence strategies in patients with HCC.

In this study, approximately 19.51% of patients with HCC (88/451) were classified as M2 grade. Our results suggesting significant differences in OS and RFS between

M0-1 and M2 patients, with a shorter RFS and OS observed in M2 patients. These results are comparable with Yao et al. ^[9^] who reported similar findings, which may be related to the similar baseline settings in both studies. We used LASSO, Boruta, XGboost, and Best_subset to screen the four machine learning methods by five-fold cross-validation and used the intersection to derive the most important factors related to M2.

Complement C3 is a plasma protein synthesized by the liver and macrophages and is a key component of the complement system. C3 has the highest complement content and plays a central role. The complement system is an important component of the immune system and can be activated through multiple pathways. C3b binds to the surface of the pathogen and facilitates its clearance through multiple pathways. In addition, complement C3 participates in processes such as cytotoxicity, the inflammatory response, and the removal of pathogenic microorganisms to enhance the body's ability to resist infection. Moreover, complement C3 increases vascular permeability, virus neutralization, and cytolysis. C3 is an important component of the immune system and plays an important role in maintaining immune balance and resisting infection. Studies have shown that serum fatty acids, adipose tissue-derived cytokines, and gut-derived endotoxins participate in complement activation. Following complement activation, C3 interacts with different types of hepatic innate immune cells, ultimately participating in the pathogenesis of non-alcoholic fatty liver disease (NAFLD).^[33^] Other studies have shown that the liver synthesizes most blood proteins, except γ -globin, liver damage reduces the synthesis of C3 and C4, and that hepatitis B virus (HBV) infection may induce the formation of various antigen–antibody complexes, which activate the complement system, leading to excessive consumption of complement components including C3 and C4. It is well known that HBV infection, as well as NAFLD, are important factors in the development of HCC.^[34^] Other studies have shown that PIWIL1 induces HCC cells to secrete complement C3, which mediates HCC through the interaction of cells and myeloid-derived suppressor cells (MDSCs), initiating the expression of the immunosuppressive cytokine IL-10. Neutralizing IL-10 secretion reduces the immunosuppressive activity of MDSCs in the HCC microenvironment with PIWIL1 expression, thereby promoting the initiation, development, and progression of HCC.^[35^] However, few studies have investigated the association between complement C3 and MVI. In our study, the OR for complement C3 was < 1, indicating that complement C3 is a protective factor.

Studies have shown that tumor aggressiveness increases with an increase in tumor size, and that tumor size is correlated with MVI.^[36^] A study from an international multicentre database showed that the incidence of MVI increased with the size of the resected HCC tumors. [37]Histological examination revealed that venous invasion-positive tumors had a strong tendency for invasion, resulting in an irregular tumor margin irregular and incomplete capsule. A radiomics-based study demonstrated that tumor size and incomplete capsules were highly reliable predictors of MVI.^[38^, 39^] In a prediction model, Chen et al.^[40^] showed that an incomplete tumor capsule is an important factor for predicting M2. Similarly, in our study, an incomplete tumor capsule was shown to be an independent risk factor for M2.

The Edmondson-Steiner grade is used to evaluate the malignancy of HCC based on the morphological and histological characteristics of tumor cells, and grades I and II represent high levels of differentiation, while grades III and IV represent poorly differentiated and undifferentiated tumors, respectively. The Edmondson-Steiner grade has been shown to be associated with the prognosis of HCC.[41]MVI and Edmondson-Steiner grades are important components of the pathological grade of HCC. Several predictive models have linked the Edmondson–Steiner classification to MVI.^[42^] Our study found that clinical Edmondson–Steiner type was significantly associated with M2 and was an independent predictor of M2 grade, implying that a Edmondson–Steiner classification indicating poor cellular differentiation corresponds to a worse MVI grade.

In a recent randomized controlled study of BCLC-A stage, a tumor diameter of 5 cm was associated with high-risk MVI. The disease-free survival rates at 1, 2, 3, and 5 years were 86.7%, 76.7%, 60.0%, and 56.3%, respectively. In contrast, in the intention-to-treat population, the survival rates in the surgery-alone group were 90.0%, 66.7%, 52.8%, and 45.7%, respectively (P = 0.448). No statistically significant difference in the survival outcome was observed between neoadjuvant radiotherapy and upfront surgery in patients with early HCC and those with a high MVI risk.^[43^] Studies have also shown that postoperative adjuvant transcatheter arterial chemoembolization(TACE) is beneficial for patients with middle- and advanced-stage HCC and MVI.^[44^] Ueshima K et al. ^[45^] showed that hepatic arterial infusion chemotherapy(HAIC) is a potential first-line treatment option for patients with advanced HCC and MVI but without distant metastasis. In a recent study, Wang K et al. [46]showed significantly prolonged RFS with MVI (median RFS: 27.7 months vs 15.5 months; HR: 0.534, 95% CI: 0.360–0.792; P = 0.002). Many studies have shown that for MVI and high-risk, resectable HCC patients, active preoperative neoadjuvant therapy other than radiotherapy may improve RFS and OS; therefore, more aggressive treatment options should be adopted for patients with M2 grade HCC.^[31^]

The nomogram is considered a user-friendly and practical prediction tool with high accuracy and good discriminative power and is widely used for the evaluation of prognostic or prognostic events.^[47^] Therefore, we developed a nomogram combining C3 level, tumor size, tumor envelope integrity, and Edmondson–Steiner grade to predict M2. According to the optimal calibration curve, the predicted probabilities were in good agreement with the actual observed values. In training set, the AUC was 0.765 (95% CI: 0.696–0.843), and in validation set, it was 0.807 (95% CI: 0.712–0.903). The optimal cut-off probability obtained using the ROC curve was 0.185, and the M2 total score was calculated according to the nomogram. The DCA results showed that the use of a nomogram to predict M2 may add more benefit than treating all patients, with or without any patients in the training and validation sets. The CIC results indicated that the predictive power of the nomogram model was significant.

It is important to note that this study had several limitations. First, all patients with HCC in our study tested positive for HBsAg. Although the established nomogram showed satisfactory discriminatory performance, our prediction model may only be applicable to HCC caused by HBV and does not consider HCC caused by hepatitis C virus or non-alcoholic/alcoholic hepatitis.^[48^] Second, this study had a small sample size, was retrospective, and inevitably had case–selection bias. Therefore, a prospective study with a balanced population and a large sample size is required to confirm the reliability of our nomogram. Third, our study was conducted in one study unit without any validation; it is necessary to validate our results using data from multiple centres. Finally, because the model was based on clinicopathological data, the mechanisms leading to early relapse and metastasis in M2 patients remain unclear, and the accuracy may be further improved if specific markers of M2 are added.

In conclusion, complement C3, tumor size > 5 cm, incomplete tumor capsule, and Edmondson–Steiner stages III–IV were identified as important predictors for the development of M2 in patients with HCC. We then combined the above variables to create a practical nomogram to make the individualized prediction of the M2 rank more objective and accurate. According to the nomogram scoring system, if patients are considered to be at a high risk of M2, more aggressive and precise treatment is tailored to reduce the potential risk of recurrence. Finally, our nomogram could improve individualized treatment designs and facilitate the selection of surveillance plans to develop more effective treatment options for patients with HCC.

AFP: Alpha fetoprotein

ALP: Alkaline phosphatase

ALT：Alanine aminotransferase

AST：Aspartate aminotransferase

AUROC： Area Under the Receiver Operating Characteristic Curve

BCLC：Barcelona Clinic Liver Cancer

CI： Confidence interval

CIC： Clinical Impact Curve

CT： Computed tomography

DCA： Decisive cure analysis

HAIC： Hepatic Artery Infusion Chemotherapy

HCC： Hepetacullar carcinoma

HR： Hazard Ratio

MRI： Magnetic resonance imaging

MVI： Microvascular invasion

OR： Odds ratio

RBC： Red blood cells

ROC： Receiver operating characteristic

TACE: Transcatheter arterial chemoembolization

WBC: White blood cells

Ethics approval and consent to participate

This study adhered to the Declaration of Helsinki and was approved by the Ethics Committee of Guangxi Medical University Cancer Hospital (Clinical Trial Number:KY2024454). Because this was a retrospective observational study and had no potential harm to the rights or welfare of subjects, the ethics committee approved the waiver of written informed consent.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This work was supported by grants from the National Natural Science Foundation of China (82260345), the Key Research and Development Program of Guangxi (AB22080066), the "139" Plan for Training High-level Backbone Medical Talents in Guangxi (G202003008), the Guangxi Medical University Outstanding Young Talents Training Program , Scientific Research Project of Hunan Provincial Health Commission(No: 202204013436),and the ScientificResearch Project of shaoyang city Science and Technology Bureau(NO:2022GZ4161)

Authors' contributions

Guoyi Xia，Zeyan Yu and Jie Chen contributed to the conception and design of the study and drafted the manuscript; Shaolong lu participated in data collection and literature research; and Xiaobo Wang ，Yuanquan Zhao contributed to data analysis and interpretation. All authors read and approved the final manuscript.

Acknowledgments

We would like to acknowledge the statistical assistance offered by Qingyuan Zhang，Jie Lin，Peng Zhu，Hualin Wu.

Author information

Guoyi Xia and Zeyan Yu have contributed equally to this work.

Authors and Affiliations

Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, 530021 Guangxi, China.

Guoyi Xia,Zeyan Yu,Shaolong Lu,Xiaobo Wang&Jie Chen

Department of Hepatobiliary Surgery, The Central Hospital of Shaoyang,

Shaoyang,422000 Hunan,China.

Guoyi Xia

Department of Hepatobiliary Surgery, Guangxi Zhuang Autonomous Region People's Hospital, Nanning, 530021 Guangxi, China.

Yuanquan Zhao

Corresponding author

Correspondence to Jie Chen.

Email: [email protected]

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No competing interests reported.

SupplementaryMaterials.docx

Prediction of M2 with early-stage hepatocellular carcinoma based on Nomogram

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Patients and data collection

Data collection and processing

Screening for indicators related to M2

Statistical analysis

Results

Screening feature variables based on machine learning

Construction and validation of model

Evaluation and application of the model

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1